Making changes on image classifucation

app.py

@@ -1,6 +1,6 @@
 '''Imports'''
 import tensorflow as tf
-import requests
+#import requests
 from transformers import pipeline
 import gradio as gr
 
@@ -8,8 +8,8 @@ import gradio as gr
 inception_net = tf.keras.applications.MobileNetV2()
 
 '''Making request and set database'''
-response = requests.get("https://git.io/JJkYN")
-tags = response.text.split("\n")
+#response = requests.get("https://git.io/JJkYN")
+#tags = response.text.split("\n")
 
 '''Define model and classify pipelines'''
 trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
@@ -17,10 +17,13 @@ classify = pipeline("text-classification", model="pysentimiento/robertuito-senti
 
 '''Define functions for demo'''
 def classify_image(inp):
-  inp = inp.reshape((-1,224,224,3))
+  inp = inp.reshape((-1, 224, 224, 3))
   inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
-  prediction = inception_net.predict(inp).flatten()
-  confidences = {tags[i]: float(prediction[i]) for i in range(1000)}
+  prediction = inception_net.predict(inp).reshape(1,1000)
+  pred_scores = tf.keras.applications.mobilenet_v2.decode_predictions(prediction, top=100)
+  #prediction = inception_net.predict(inp).flatten()
+  confidences = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)}
+  #confidences = {tags[i]: float(prediction[i]) for i in range(1000)}
   return confidences
 
 def audio_to_text(audio):
@@ -34,8 +37,6 @@ def text_to_sentiment(text):
 demo = gr.Blocks()
 
 '''Making demo'''
-demo = gr.Blocks()
-
 with demo:
   gr.Markdown("Second Demo with Blocks")
   with gr.Tabs():